Inspection systems identify and classify defects on semiconductor wafers to generate a defect population on a wafer. A given semiconductor wafer may include hundreds of chips, each chip containing thousands of components of interest, and each component of interest may have millions of instances on a given layer of a chip. As a result, inspection systems may generate vast numbers of data points (e.g. hundreds of billions of data points for some systems) on a given wafer. Further, the push for ever-shrinking devices leads to increased demands on inspection systems. The demands include the need for increased resolution and capacity without sacrificing inspection speed or sensitivity.
The sensitivity of defect detection is critically dependent on sources of noise in the defect detection method. For example, typical defect detection systems generate a difference image between a test image and a reference image in which defects in the test image are manifest as a difference between pixel values in the test image and the reference image. However, noise associated with the reference image and/or the test image reduces the defect detection sensitivity. Some additional defect detection systems utilize multiple reference images (e.g. from different wafers, different dies, different regions of a repeating pattern within a die, or the like) in an attempt to increase the sensitivity. Even so, such systems are inherently susceptible to reference data noise, which ultimately limits the defect detection sensitivity. Therefore, it would be desirable to provide a system and method for curing shortcomings such as those identified above.